Datasets. In order to simulate the various domain shifts, we consider three popular large-scale autonomous driving datasets with considerable domain gaps among them, The Waymo Open Dataset [1]}, the KITTI dataset [2]}, and the nuScenes dataset [3]}. The largest dataset among these is Waymo, with more than 230K annotated lidar frames collected across six US cities, of which we use approximately 50K due to memory constraints in a 40K/10K training/validation (test) split. The nuScenes dataset consists of 34,149 frames which we utilize in 28K/6K split. The smallest dataset is KITTI, consisting of 7,481 (3K/3K split) annotated lidar frames collected from Germany. All training and validation splits used are official and consistent with that used by other works in the 3D object detection literature.
